, 2006). Even very brief periods of sound exposure can induce new perceptual skills when the acoustic features have a reliable statistical structure (e.g., a high probability that two sounds occur sequentially).

After only 2 min of experience, infants can discriminate familiar syllable sequences from novel ones, including those from a natural language (Saffran et al., 1996 and Pelucchi et al., 2009). This process of statistical learning may require a certain degree of attention and social interaction (Toro et al., 2005 and Kuhl, 2007). Studies focused on the emergence of vocal behavior in songbirds have demonstrated the importance of early sensory exposure to natural communication sounds on adult perception. In zebra finches, hearing vocalizations begins to influence auditory perception and vocal behavior shortly after auditory brainstem thresholds mature (Amin et al., 2007). Starting at posthatch day 20, juveniles PF-02341066 concentration memorize the songs of adult tutors. This period of auditory learning generates the perceptual templates used for motor learning of vocal production by males. In addition to vocal

ERK inhibitor chemical structure learning, both males and females remember the songs that they hear frequently during development and are attracted to similar sounds in adulthood. For example, zebra finches develop preferences for hearing conspecific song over heterospecific song based on juvenile early experience, and females sexually imprint on the songs that they hear as juveniles (Miller, 1979, Peters et al., 1980, Clayton, 1988, Clayton and Prove, 1989,

Nagle and Kreutzer, 1997, Riebel et al., 2002 and Lauay et al., 2004). Cross-fostering studies provide additional support for the idea that perceptual preferences are shaped by hearing communication vocalizations during development. Both males and females that are raised by adults of another species or subspecies fail to show consistent preferences for conspecific Carnitine dehydrogenase songs as adults and show increased attraction to heterospecific songs (Immelmann, 1969, Clayton, 1988, Clayton, 1990 and Campbell and Hauber, 2009). These studies suggest that juvenile exposure to adult communication sounds influences auditory system maturation, but we do not yet know how to relate the effects of vocal experience on behavior to the functional development of auditory circuits and cellular properties (below). In adults, auditory training on a variety of perceptual tasks inevitably leads to improvement in performance (Recanzone et al., 1993, Wright et al., 1997, Ari-Even Roth et al., 2003, Beitel et al., 2003, Brown et al., 2004, Sakai and Kudoh, 2005, Rutkowski and Weinberger, 2005, Mossbridge et al., 2006, Polley et al., 2006, Blake et al., 2006, van Wassenhove and Nagarajan, 2007, Draganova et al., 2009, Ilango et al., 2010, Bieszczad and Weinberger, 2010 and Comins and Gentner, 2010).

In particular, downregulation of the lipid raft organizing protein Flotillin (also known as Reggie; Otto

and Nichols, 2011) renders neurons insensitive to Sema3A-mediated growth cone collapse (Carcea et al., 2010). There are multiple examples of the need for lipid raft partitioning for directional responsiveness to Selleckchem S3I 201 guidance cues (Guirland and Zheng, 2007), and endocytosis could be one of the cellular responses that differ for receptors found in rafts or not in rafts. These results suggest the possibility that neurons can use an “internalization switch” such that intrinsic differences in signaling responses downstream of common guidance cues regulates extent of endocytosis and thus responsiveness to guidance cues (Carcea et al., 2010). One can also envision how the same cell could throw the internalization switch differently at different developmental junctures, or how different parts of the same neuron could respond differently to the same cues (for example, Polleux and Ghosh, 2002 and Shelly et al., 2011) using an internalization

switch. Many receptors that have more than one ligand show ligand-specific responses upon activation. What could be a cellular mechanism explaining this observation? A recent study of differential signaling outcomes resulting from NGF and NT-3 binding to the TrkA receptor provides a beautiful example (Harrington et al., 2011). NGF and NT-3 both bind and activate TrkA receptor to promote axonal extension (Kuruvilla et al., 2004) and activate E7080 manufacturer also multiple known downstream effectors of TrkA. NT-3 is secreted by intermediate targets of sympathetic neurons and mediates signaling important for local axon extension, while NGF is produced in final target fields of sympathetic neurons and supports neuronal survival via retrograde signaling. Only NGF-induced internalized NGF/TrkA endosomes are capable of eliciting retrograde survival signaling. Harrington et al. (2011) discovered that NGF/TrkA endosomes, but not NT-3/TrkA endosomes, recruit and activate rac1 and cofilin, a microfilament-depolymerizing factor. Activation

of rac1 on early endosomes and activation of cofilin are necessary and sufficient for maturation of TrkA-containing early endosomes to retrogradely-transporting signaling endosomes. The authors also showed that NT-3 binds inefficiently to Trk under acidic environment, such as that in the early endosome, and by a mechanism that remains to be defined, dissociation of NT-3 from TrkA in the endosome prevents recruitment of rac1, even though activation of other signaling cascades is sustained. These data suggest that differential sensitivity to endosomal acidification underlies the differences in the capability of NGF/Trk and NT-3/Trk endosomes to elicit retrograde survival signaling and beautifully highlight the regulatory power of postendocytic events in signaling endosomes.

To test whether ApNRX and ApNLG are

required for the stable maintenance of LTF, we injected antisense oligonucleotides directed against ApNRX into sensory neurons at 24 hr after repeated pulses of 5-HT and measured EPSPs at 48 hr and 72 hr. Basal synaptic transmission was not affected by the oligonucleotide injections ( Figure 7A; % initial EPSP amplitude: no injection at 24 hr 8.6 ± 8.2, at 48 hr –4.1 ± 8.0, at 72 hr –4.2 ± 9.3, n = 14; antisense alone Z VAD FMK at 24 hr 3.5 ± 7.1, at 48 hr –17.5 ± 5.7, at 72 hr –13.7 ± 7.8, n = 11; sense alone at 24 hr –0.7 ± 5.5, at 48 hr –12.3 ± 5.6, at 72 hr –8.6 ± 10.0, n = 11). However, we find that injection of antisense oligonucleotides leads to a significant reduction of LTF measured at 48 and 72 hr ( Figure 7A; % initial EPSP amplitude: 5-HT at 24 hr 99.2 ± 15.2, at 48 hr 75.1 ± 9.7, at 72 hr 52.2 ± 10.8, n = 16; 5-HT + antisense at 24 hr 89.2 ± 15.3, at 48 hr 24.6 ± 10.5, at 72 hr 10.6 ± 8.9, n = 19, p <

0.001 versus 5-HT at 48 and 72 hr; 5-HT + sense at 24 hr 103.7 ± 22.7, at 48 hr 69.0 ± 16.1, Vorinostat at 72 hr 56.8 ± 15.8, n = 14). We also injected antisense oligonucleotides directed against ApNLG into the motor neuron at 24 hr after repeated pulses of 5-HT and measured EPSPs at 48 hr and 72 hr. We again found that injection of antisense oligonucleotides leads to a significant reduction of LTF measured at 48 and 72 hr (Figure 7B; Ketanserin % initial EPSP amplitude: 5-HT at 24 hr 115.2 ± 20.6, at 48 hr 81.5 ± 16.5, at 72 hr 67.2 ± 10.3, n = 11; 5-HT + antisense at 24 hr

89.8 ± 11.3, at 48 hr 18.6 ± 6.1, at 72 hr 1.8 ± 5.4, n = 18, p < 0.001 versus 5-HT at 48 and 72 hr; 5-HT + sense at 24 hr 103.7 ± 20.5, at 48 hr 103.0 ± 22.0, at 72 hr 78.0 ± 5.5, n = 11). Basal synaptic transmission was not affected by the oligonucleotide injections (% initial EPSP amplitude: no injection at 24 hr 4.1 ± 4.0, at 48 hr –13.6 ± 5.9, at 72 hr –14.4 ± 5.2, n = 7; antisense alone at 24 hr 8.4 ± 6.6, at 48 hr 5.0 ± 8.9, at 72 hr –1.9 ± 8.1, n = 7; sense alone at 24 hr 15.1 ± 10.7, at 48 hr 7.8 ± 8.3, at 72 hr 7.8 ± 10.6, n = 9). These results indicate that ApNRX and ApNLG play a critical role not only in the initiation of LTF and the associated presynaptic structural changes but also in the stabilization and persistence of these learning-induced synaptic changes. The human NLG-3 R451C point mutation has been linked to ASD (Jamain et al., 2003). Since there has not been any study investigating the mutant’s effect in synaptic plasticity, we made an arginine (R) to cysteine (C) point mutation in ApNLG at the homologous position, overexpressed this mutant in the motor neurons, and investigated its effect on 5-HT-induced changes in the strength of the sensory-to-motor neuron synapse.

For the electrical network, we demonstrate higher-than-predicted electrical clustering and anticlustering coefficients of triplet and quadruplet patterns, supported by the confinement of electrical connections within the sagittal plane. For the chemical network, we show that transitive chemical connectivity motifs are overrepresented,

with feedforward (FF) motifs being supported by a specific spatial arrangement along the sagittal plane. Finally, we find that the electrical and chemical networks are not independent at the pair and the triplet level. www.selleckchem.com/products/OSI-906.html Together, these results indicate that the connectivity of the interneuron network is highly organized, which has important implications for the structure of activity patterns in the network. The first evidence that neural networks are different from random networks—and exhibit small-world properties—was provided by Watts and Strogatz (1998) who used the clustering coefficient to quantify network topology. High clustering coefficients have been reported in the brain of C. elegans ( Varshney et al., 2011 and Watts and Strogatz, 1998) and extrapolated for the cortical pyramidal cell network ( Perin et al., 2011). Our results provide evidence for higher-than-expected clustering in

a network of only interneurons, for both electrical and chemical connectivity. The high degree of clustering in the electrical patterns compared to random connectivity models provides strong evidence that gap junction networks exhibit clustered features in the vertebrate nervous system, as selleck products they do in C. elegans ( Varshney et al., 2011). Although electrical connections are widespread in the mammalian brain ( Bartos et al., 2002, Galarreta and Hestrin, 1999, Gibson et al., 1999, Koós and Tepper, 1999, Landisman et al., 2002 and Venance et al., 2000; for review, see Connors to and Long, 2004), the presence of clustered motifs in a single cell type has not previously been tested

directly. Nevertheless, the dense interconnectivity mediated by gap junctions ( Fukuda, 2009), the spatial organization of electrical coupling ( Alcami and Marty, 2013 and Amitai et al., 2002), and the segregation by cell type observed for interneurons in the cortex, striatum, and cerebellum ( Blatow et al., 2003, Gibson et al., 1999, Hull and Regehr, 2012 and Koós and Tepper, 1999) suggest that clustered electrical connectivity may be a general feature of interneuron networks in the mammalian brain. We demonstrate that the interneuron chemical network also exhibits higher-than-expected clustering, as well as a preference for transitive triplet motifs. The notion of transitivity is commonly used in graph theory (Bang-Jensen and Gutin, 2008), and various complex networks have been proposed to favor locally transitive patterns, such as social networks and the World Wide Web (Holland and Leinhardt, 1970, Milo et al., 2002 and Milo et al., 2004).

These studies also depend on photocurrent stability of inhibitory opsin function on mammalian behavioral timescales. The crystal structure

of NpHR has been published (Kouyama et al., 2010) and illustrates that this protein has a high degree of structural homology within the retinal binding pocket with the proton pumps such as bacteriorhodopsin. In 2010 two groups explored the use of proton pumps (Mac, Arch, and eBR) as optogenetic tools (Chow et al., 2010 and Gradinaru et al., selleck compound 2010), finding robust efficacy but leaving open questions of long-term tolerability and functionality of proton-motive pumps in mammalian neurons. One caveat is the extent to which pumping of large proton fluxes to the extracellular space (especially in juxtamembranous compartments difficult to assess) might have unwanted or non-cell-type-specific effects; such an effect might manifest only under conditions where many (but not all) local neurons are expected to be opsin expressors, and might be detected in this case (e.g., in extracellular recordings)

as optogenetic BGB324 solubility dmso inhibition of spiking in nonexpressing cells with a slower mean timecourse than expected from the millisecond-scale kinetics of the pumps. Indeed, the inhibitory pumps (including chloride pumps) are typically driven with continuous light (to avoid rebound excitation), which could deter recovery of ionic or pH imbalances; in contrast, channelrhodopsins are permeant to cations including protons but are driven most typically in neuroscience experiments by well-separated pulses of light. Finally, caution must be exercised, particularly with steady light, to avoid heating of tissue. It is therefore important to consider the light intensities required for optogenetic inhibition at a particular photocurrent value, keeping in mind that to compensate for scattering losses, in vivo light is typically delivered

to the tissue at 100-fold or more higher intensity than required at the target Parvulin cell (Aravanis et al., 2007 and Gradinaru et al., 2010). To avoid toxicity while maintaining efficacy, we recommend selecting inhibitory opsins that allow delivery of > 400 pA of current at irradiance values of < 10–20 mW/mm2 at the target cell, and we return to the issue of heating and irradiance levels below. While nanoampere-scale inhibitory currents sufficient for mammalian behavioral effects already can be recruited at < 5 mW/mm2 (Gradinaru et al., 2010), ongoing engineering and discovery of known and existing opsins will continue to expand the optogenetic toolkit in this direction as well. Just as with NpHR as described above, modifying Arch by providing the ER2 motif for endoplasmic reticulum export—initially found by Gradinaru et al. (2008) and Zhao et al. (2008) to promote microbial opsin expression and function in neurons—allows generation of larger proton currents (J.

This is often called fictive learning ( Hayden et al., 2009). When sequences switched,

actions in the sequence following the switch that were the same as actions in the sequence that preceded the switch were given the value they had before the sequence switched. In other words the values were copied into the new block. This was consistent with the fact that the animal did not know when the sequence switched and so it could not update its action values until it received feedback that the previous action was no longer correct. Actions from the previously correct sequence that were not possible in the new sequence were given a Obeticholic Acid value of 0. The learning rate parameters ρf and an additional inverse temperature parameter, β, were estimated separately for each session by minimizing the log-likelihood of the animals’ decisions using fminsearch in Matlab, as we have done previously ( Djamshidian et al., 2011). If β is small, then the animal is less likely to pick the higher value target whereas if β is large the animal is more likely to pick DAPT clinical trial the higher value target,

for a fixed difference in target values. To estimate the log-likelihood we first calculated choice probabilities using: equation(Equation 2) di(t)=eβvi(t)∑j=12eβvj(t). The sum is over the two actions possible at each point in the sequence. We then calculated the log likelihood (ll) of the animal’s decision sequence as equation(Equation 3) ll=−∑t=1Tlog(di(t)ci(t)+(1−di(t))(1−ci(t))). The sum is over all decisions in a recording session, T. The variable ci(t) models the chosen action and has a value of one for action 1 and 0 for action 2. Average optimal values for β were 1.858 ± 0.03

and 1.910 ± 0.025 for monkeys 1 (n = 34 sessions) and 2 (n = 61 sessions), respectively. Average optimal values for ρf = positive were 0.440 ± 0.015 and 0.359 ± 0.008 for monkeys 1 and 2. Average optimal values for ρf = negative were 1.042 ± 0.03 and 0.656 ± 0.013 for monkeys 1 and 2. The value of the action that was taken, vi(t), was then correlated with neural activity in the ANOVA model. We modeled the integration of sequence or learned action value and color bias information why on choices in the fixed condition. We used action value as an estimate of sequence learning, because knowing the sequence entails knowing the actions. Although it is possible that some actions are known before the complete sequence, the structure of the task is such that knowing actions and sequences are highly correlated. Further, we found that the behavioral weight estimated by action value significantly predicted sequence representation in lPFC neurons (Figure 9). We estimated the relative influence of action value and color bias information by using logistic regression to predict the behavioral performance (fraction correct or fc) as a function of color bias (CB) and action value.

67 ± 0.67 mm, which was determined as the MIC. The largest inhibition zone (11.67 ± 0.33 mm) was observed at the highest concentration of oil applied (50%; 500 μl/ml), which was probably due to the higher concentration of active chemical components in the EO fraction. In the positive control (microwell filled with a 1000 mg/l chloramphenicol solution), an average inhibition zone of 16.67 mm was observed. In the negative control (microwell filled with DMSO without EO), no inhibition zones were observed, suggesting that there was no interference from the diluents used in the tests. The morphological cell damage of C. perfringens caused by treatment with S. montana EO at a MIC concentration are shown in the transmission

electron micrographs in Fig. 2. The micrographs Dinaciclib in vivo of untreated cell culture (A and B) without exposure to EO showed continuous thin, smooth cell walls and other defined cellular structures. The C. perfringens cells treated with EO (C, D, E and F) had selleck kinase inhibitor adulterated morphology, where cell walls had irregularities, less smoothness, less uniformity and

degenerative changes leading to wall ruptures and subsequent cellular lysis in some cases. An unequal cytoplasm distribution caused by the clumping and agglomeration of intracellular material was observed in the treated cells (indicated by arrows). Furthermore, the cells lacked cytoplasm in certain regions due to the loss of membrane functionality, which was characteristic of the mechanism of action of the major chemical components of S. montana EO. The primary events of the sporulation process were not observed due to contact found of the EO with viable cells from the microorganism studied. The population variations of C. perfringens type A viable cells in mortadella-type sausages formulated with different concentrations of S. montana EO and levels of NaNO2 during storage at 25 °C for 30 days are shown in Table 2. In mortadella elaborated without the addition of EO and nitrite (control samples), the C. perfringens populations increased reaching 8.95 log10 CFU/g at the first day of storage. After 10 days of storage, the counts

were decreased in the control samples, showing a population of 4.83 log10 CFU/g at the end of the storage period. The samples formulated without nitrite and with 0.78% EO had populations that were not significantly different (p > 0.05) than the initial inoculum at the first day of storage. However, their growth was restricted (p ≤ 0.05) when compared to the control (bacteriostatic effect) at the first day. In the mortadella with 1.56% EO (MIC concentration) without nitrite, we observed a decrease of 1.02 log10 CFU/g on the first day of analysis showing a antimicrobial effect of the EO evaluated at concentrations higher than 1.56% added in sausages. The most drastic effect was observed in samples elaborated with 3.125% EO without NaNO2 where the bacterial population was reduced to 4.65 log10 CFU/g after 24 h of storage.

, 2007 and Zhang and Yan, 2008). However, similar studies have not yet been done in the auditory system after operant discrimination training. In the visual system, there is some evidence that map expansions after training may either develop or renormalize at different rates in secondary versus primary cortical areas (Ghose et al., 2002 and Yang and Maunsell, 2004). More

studies are necessary to determine whether Caspase inhibitor review plasticity develops in multiple brain regions, whether plasticity renormalizes at the same rate in different brain regions, and what factors may inhibit or enhance expansion and renormalization. Although the map renormalization stage has been less well-studied than map expansion, several recent studies have reported renormalization after behavior training. In our study, we found map expansions renormalized 35 days after the beginning of low-frequency discrimination training or NBS pairing with low-frequency

tones. Similar map renormalization has now been observed in the auditory, visual, and motor cortex (Ma et al., 2010, Molina-Luna et al., 2008, Takahashi et al., 2010 and Yotsumoto et al., 2008). Renormalization has also been observed after the cortical plasticity associated with recovery from stroke and brain injury (Tombari et al., 2004 and Ward et al., 2003). These findings indicate that cortical map expansion is not usually this website the method by which skills are permanently stored in the brain but rather that map expansion is an important mechanism to generate efficient circuitry to perform behaviorally important tasks. The time required to pass through the map expansion and map renormalization

stages is likely to be affected by many factors. Many studies have shown that rates of learning and map expansion are affected by task difficulty and by motivation (Rutkowski and Weinberger, 2005). Demanding tasks are more below likely to cause plasticity because they lead to increased neuromodulator release compared to tasks that are easy to perform (Arnold et al., 2002 and Himmelheber et al., 2000). We propose that when subjects are required to perform demanding tasks or are highly motivated, they may transition to the map renormalization stage more slowly than when subjects are required to perform easy tasks. In some previous studies, map expansions persisted for several months after the beginning of behavior training, implying that these subjects never reached the map renormalization stage (Polley et al., 2006, Recanzone et al., 1992a, Recanzone et al., 1992b and Recanzone et al., 1993). In studies with persistent map expansions, subjects were often trained using adaptive tracking so that the difficulty of the task changed on a trial-by-trial basis and subjects never achieved >70% correct performance during a session.

Experimental data were previously obtained in the horizontal velocity-to-position neural integrator of the awake, behaving adult goldfish (Aksay et al., 2000, Aksay et al., 2001, Aksay et al., 2003 and Aksay et al., 2007). Briefly, neuronal tuning curves were determined from extracellular recordings of integrator neuron activity. They were well approximated by a threshold-linear relationship between firing rate r  i and eye position E   during stable fixations, equation(Equation 1) ri=maxki(E−Eth,i),0=max(kiE+r0,i),0,ri=maxki(E−Eth,i),0=max(kiE+r0,i),0,described for a given cell i   by a sensitivity k  i and either eye-position

threshold Eth,iEth,i or intercept r0,ir0,i ( Figure 2A). Neuronal excitability was determined from intracellular recordings of the response to current buy SP600125 injection ( Figure 2D). Circuit interactions were assessed by extracellular recording of single-unit activity immediately

following localized pharmacological silencing of neighboring cells using lidocaine or muscimol. Neuronal drift patterns characterizing the effects of pharmacological inactivation were obtained by comparing firing rate drifts before and after inactivation (Supplemental Methods). Drift was plotted as a function of firing rate rather than eye position to eliminate potential confounds that could occur if the inactivations affected the PI3K Inhibitor Library ic50 eye position readout from the circuit by altering the relationship between firing rates and eye position. To pool across cells recorded in different preparations, neuronal activity was normalized using the eye-position sensitivities and intercepts given by the steady-state (control) tuning curve relationships (Equation 1). Firing rates for cell i   were normalized

by first subtracting its primary rate r0,ir0,i and then many dividing by its position sensitivity ki, resulting in normalized rates in units of eye position. Firing rate drifts were normalized by the position sensitivity ki. An identical analysis was performed on the model firing rate data, permitting a direct comparison between experiment and theory. The model circuit contained 100 conductance-based neurons: 25 excitatory and 25 inhibitory neurons on each side of the midline. Tuning curves ri(E)ri(E) for 37 of the neurons were taken directly from the experimental measurements, with the other 63 generated by varying the slopes k and thresholds Eth of the experimental ones by uniformly distributed factors between 0.9 and 1.1, and −1° and 1°, respectively. Tuning curves of excitatory and inhibitory neurons were drawn from the same distribution.

Hamasaka et al. (2007) proposed that glutamate inhibits LNv activity via the metabotropic mGluRA glutamate receptor. Quisinostat in vivo They also showed that light avoidance levels are increased in mGluRA mutant larvae, although they did not determine the relevant cells ( Hamasaka et al., 2007). However, our

gene expression profiles from purified larval LNvs revealed that they also express the glutamate-gated chloride channel GluCl ∼2.5-fold more highly than in Elav+ neurons (M. Ruben & J.B., unpublished data). Adult l-LNvs also have functional GluCl channels, although their behavioral role is unknown ( McCarthy et al., 2011). To test whether glutamate regulates light avoidance in LNvs via GluCl or mGluRA, we used RNAi to reduce expression of each receptor. Both transgenes reduce expression of their target (Hamasaka et al., 2007 and Figure S4C). We found that Pdf > GluClRNAi larvae had significantly increased light avoidance at 150 lux, whereas Pdf > mGluRARNAi and control larvae did not avoid light ( Figure 5C). Thus, reducing GluCl INCB018424 in LNvs phenocopies reducing glutamate release from DN1s. Next, we tested the roles of GluCl and mGluRA in regulating circadian behavior. Our data show that Pdf > GluClRNAi larvae had no light avoidance rhythm, with levels of light avoidance

constitutively high ( Figure 5D), whereas Pdf > mGluRARNAi larvae display rhythmic light avoidance ( Figure 5D). Thus, GluCl is required in LNvs for rhythmic

light avoidance. We propose that DN1s rhythmically release glutamate, which is perceived via GluCl in LNvs to mediate rhythmic inhibition of LNv neuronal activity. We have subsequently found that mGluRA helps synchronize LNv molecular clock oscillations (B.C. and J.B., unpublished data). To directly test whether GluCl can inhibit LNv activity, we measured the responses of dissociated larval LNvs expressing the intracellular Ca2+ sensor GCaMP1.6 (Reiff et al., 2005) to directly applied neurotransmitters. ACh produced by Bolwig’s Urease organ is required for larval light avoidance (Keene et al., 2011). Applying ACh to dissociated LNvs increased intracellular Ca2+ levels, as previously reported (Dahdal et al., 2010 and Wegener et al., 2004), as measured by increased GCaMP fluorescence (Figures 5E and 5F). ACh increases intracellular Ca2+ in LNvs by activating nicotinic ACh receptors to produce excitatory postsynaptic potentials, eventually causing depolarization. In turn, this increases cytoplasmic Ca2+ via voltage-gated Ca2+ channels (Dahdal et al., 2010 and Wegener et al., 2004), which is observed as increased GCaMP fluorescence. Given the relative insensitivity of GCaMP1.6 to single action potentials (Pologruto et al., 2004), these Ca2+ transients in LNvs likely reflect bursts of action potentials.